!pip install --upgrade plotly
!pip install sweetviz
!pip install dtale
!pip install numpy
!pip install pandas
!pip install pandas_profiling

import operator
import pandas as pd
import numpy as np
from datetime import datetime, date
from pandas_profiling import ProfileReport

import warnings
warnings.filterwarnings('ignore')

import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
%matplotlib inline

df = pd.read_csv("cars_dataset.csv")

df.drop(['Unnamed: 0'],axis=1,inplace=True)

df.columns

Index(['Make', 'Model', 'Price', 'Year', 'Transmission', 'Listing Type',
       'Body Type', 'Fuel Type', 'Engine Capacity - Cc', 'Engine Power - KW'],
      dtype='object')

df['Price'] = df['Price'].fillna(df['Price'].mean()).astype(int)  
df['Year'] = df['Year'].fillna(df['Year'].mean()).astype(int) 
df['Engine Capacity - Cc'] = df['Engine Capacity - Cc'].fillna(df['Year'].mean()).astype(int) 

df['Price'] = df['Price'].astype(int)
df['Year'] = df['Year'].astype(int)
df['Engine Capacity - Cc'] = df['Engine Capacity - Cc'].astype(int)

df['Engine Power - KW'] = df['Engine Power - KW'].astype('str')
df['Make'] = df['Make'].astype('str')
df['Model'] = df['Model'].astype('str')
df['Transmission'] = df['Transmission'].astype('str')
df['Listing Type'] = df['Listing Type'].astype('str')
df['Body Type'] = df['Body Type'].astype('str')
df['Fuel Type'] = df['Fuel Type'].astype('str')

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1099 entries, 0 to 1098
Data columns (total 10 columns):
 #   Column                Non-Null Count  Dtype 
---  ------                --------------  ----- 
 0   Make                  1099 non-null   object
 1   Model                 1099 non-null   object
 2   Price                 1099 non-null   int32 
 3   Year                  1099 non-null   int32 
 4   Transmission          1099 non-null   object
 5   Listing Type          1099 non-null   object
 6   Body Type             1099 non-null   object
 7   Fuel Type             1099 non-null   object
 8   Engine Capacity - Cc  1099 non-null   int32 
 9   Engine Power - KW     1099 non-null   object
dtypes: int32(3), object(7)
memory usage: 73.1+ KB

df.isnull().sum()

Make                    0
Model                   0
Price                   0
Year                    0
Transmission            0
Listing Type            0
Body Type               0
Fuel Type               0
Engine Capacity - Cc    0
Engine Power - KW       0
dtype: int64

print("Total Unique Make : ",len(df['Make'].unique()))

Total Unique Make :  96

print("Total Unique Model : ",len(df['Model'].unique()))

Total Unique Model :  711

print("Total Unique Year : ",len(df['Year'].unique()))

Total Unique Year :  87

print("Total Unique Transmission : ",len(df['Transmission'].unique()))

Total Unique Transmission :  10

print("Total Unique Listing Type : ",len(df['Listing Type'].unique()))

Total Unique Listing Type :  3

print("Total Unique Body Type : ",len(df['Body Type'].unique()))

Total Unique Body Type :  19

print("Total Unique Fuel Type : ",len(df['Fuel Type'].unique()))

Total Unique Fuel Type :  9

df.describe()

	Price	Year	Engine Capacity - Cc
count	1.099000e+03	1099.000000	1099.000000
mean	1.600981e+06	1989.533212	2450.032757
std	8.903456e+07	22.721557	1384.976653
min	-2.147484e+09	1920.000000	0.000000
25%	2.799500e+04	1970.000000	1989.000000
50%	4.999000e+04	1989.000000	1989.000000
75%	1.189875e+05	2011.000000	2800.000000
max	2.000020e+09	2021.000000	7800.000000

df.dtypes

Make                    object
Model                   object
Price                    int32
Year                     int32
Transmission            object
Listing Type            object
Body Type               object
Fuel Type               object
Engine Capacity - Cc     int32
Engine Power - KW       object
dtype: object

df

	Make	Model	Price	Year	Transmission	Listing Type	Body Type	Fuel Type	Engine Capacity - Cc	Engine Power - KW
0	Porsche	911 992 Carrera S Coupe 2dr PDK 8sp 3.0TT [MY21]	7922666	2020	nan	nan	nan	nan	1989	nan
1	BMW	i8 I12 LCI Coupe 2dr Auto 6sp AWD 1.5T/105kW ...	7922666	2019	nan	nan	nan	nan	1989	nan
2	Land Rover	Defender 90 Adventure Wagon 3dr Man 6sp 4x4 2...	10999000	2015	nan	nan	nan	nan	1989	nan
3	Porsche	911 997 Series II Carrera Cabriolet 2dr PDK 7...	7922666	2009	nan	nan	nan	nan	1989	nan
4	Porsche	Cayenne 9YB Turbo Coupe 5dr Tiptronic 8sp 4x4...	7922666	2020	nan	nan	nan	nan	1989	nan
...	...	...	...	...	...	...	...	...	...	...
1094	CHEVROLET	BELAIR	7922666	1957	Automatic	Used	Hardtop - Coupe	nan	1989	nan
1095	PLYMOUTH	ROAD RUNNER TRIBUTE	7922666	1968	Automatic	Used	Hardtop - Coupe	nan	1989	nan
1096	AC	COBRA	7922666	2001	Manual	Used	Roadster	nan	1989	nan
1097	HSV	W427 VE	7922666	2009	nan	Used	Sedan	nan	1989	nan
1098	CHEVROLET	CORVETTE C6	7922666	2008	Manual	Used	Hardtop - Coupe	nan	1989	377

1099 rows × 10 columns

Pandas Profiling Report

profile = ProfileReport(df, title="Cars Profiling Report", explorative=True)

profile

profile.to_widgets()

profile.to_file("Cars Profiling Report.html")

EDA Automated Report

import sweetviz as sv

cars_report = sv.analyze(df)

cars_report

cars_report.show_html('Automated_EDA_Cars_Dataset.html')

Dtale Report

import dtale

d = dtale.show(df, ignore_duplicate=True)
d

d.adjust_cell_dimensions(height=100)

d.open_browser()

Exploratory Data Analysis

df['Make'].value_counts()

FORD               180
MERCEDES-BENZ      118
HOLDEN             103
CHEVROLET           76
BMW                 51
                  ... 
Bentley              1
HARLEY DAVIDSON      1
Tesla                1
FLINT                1
COBRA                1
Name: Make, Length: 96, dtype: int64

df['Model'].value_counts()

 MUSTANG                                                                     17
 CORVETTE                                                                    14
 RANGE ROVER                                                                  7
 CAYENNE                                                                      7
 ROADSTER                                                                     6
                                                                             ..
 E-TYPE LIGHTWEIGHT                                                           1
 DB2/4 Mk 1                                                                   1
 COMMODORE  COMMODORE VF MOTORSPORT EDITION                                   1
 20/25 HP OA                                                                  1
 Range Rover L405 SDV8 Vogue SE Wagon 5dr Spts Auto 8sp 4x4 4.4DTT [MY19]     1
Name: Model, Length: 711, dtype: int64

df['Year'].value_counts()

1989    69
2015    46
2010    40
2013    39
2014    35
        ..
1940     1
1937     1
1953     1
1990     1
1920     1
Name: Year, Length: 87, dtype: int64

df['Body Type'].value_counts()

Sedan              289
Hardtop - Coupe    250
Wagon              115
Convertible        112
Ute                 86
nan                 54
Roadster            51
Hatchback           33
Van                 32
Fastback            18
Panel Van           12
Cab Chassis          8
Soft Top             7
Targa                7
Limousine            7
Open Wheeler         6
Tourer               5
Tray                 4
Style Side           3
Name: Body Type, dtype: int64

df['Fuel Type'].value_counts()

nan                 415
Unleaded            253
Premium Unleaded    244
Diesel              106
Leaded Petrol        66
Dual Fuel             6
LPG                   6
Ethanol               2
Hybrid                1
Name: Fuel Type, dtype: int64

df.columns

Index(['Make', 'Model', 'Price', 'Year', 'Transmission', 'Listing Type',
       'Body Type', 'Fuel Type', 'Engine Capacity - Cc', 'Engine Power - KW'],
      dtype='object')

df['Transmission'].value_counts()

Automatic                  484
nan                        312
Manual                     262
Tiptronic                   18
Manual Overdrive             7
Sequential Manual Auto       5
Sportmatic                   3
Steptronic                   3
CVT                          3
Sequential Sports Shift      2
Name: Transmission, dtype: int64

df['Listing Type'].value_counts()

Used    1041
nan       54
New        4
Name: Listing Type, dtype: int64

df['Engine Capacity - Cc'].value_counts()

1989    585
0        25
2000     21
350      16
1600     15
       ... 
307       1
4400      1
4806      1
5733      1
7000      1
Name: Engine Capacity - Cc, Length: 171, dtype: int64

df['Engine Power - KW'].value_counts()

nan       944
0          13
200         5
350         4
450         3
         ... 
5766.0      1
243         1
114         1
44          1
283         1
Name: Engine Power - KW, Length: 89, dtype: int64

Correlation

corr = df.corr()

fig = px.imshow(corr)
fig.show()

Insight 1 : Top 20 Car Makes

a = dict(df['Make'].value_counts())
insight1 = pd.DataFrame()
a = dict(sorted(a.items(), key=operator.itemgetter(1), reverse=True)[:20])
insight1['Make'] = a.keys()
insight1['Total Count'] = a.values()
insight1['Percentage'] = ((insight1['Total Count'] / 1099)*100).round(2)
insight1['Percentage'] = insight1['Percentage'].astype(str) + '%'
insight1.style.background_gradient(cmap='Reds')

	Make	Total Count	Percentage
0	FORD	180	16.38%
1	MERCEDES-BENZ	118	10.74%
2	HOLDEN	103	9.37%
3	CHEVROLET	76	6.92%
4	BMW	51	4.64%
5	JAGUAR	41	3.73%
6	HSV	30	2.73%
7	VOLKSWAGEN	30	2.73%
8	PORSCHE	30	2.73%
9	AUDI	27	2.46%
10	TOYOTA	27	2.46%
11	CHRYSLER	26	2.37%
12	PONTIAC	16	1.46%
13	LAND ROVER	16	1.46%
14	DODGE	14	1.27%
15	FERRARI	14	1.27%
16	MAZDA	14	1.27%
17	NISSAN	13	1.18%
18	Ferrari	12	1.09%
19	Porsche	11	1.0%

fig = px.treemap(insight1, path=['Make', 'Total Count', 'Percentage'], values='Total Count',title="Top 20 Car Makes")
fig.show()

Insight 2 : Top 10 Car Models

b = dict(df['Model'].value_counts())
insight2 = pd.DataFrame()
b = dict(sorted(b.items(), key=operator.itemgetter(1), reverse=True)[:10])
insight2['Model'] = b.keys()
insight2['Total Count'] = b.values()
insight2['Percentage'] = ((insight2['Total Count'] / 1099)*100).round(2)
insight2['Percentage'] = insight2['Percentage'].astype(str) + '%'
insight2.style.background_gradient(cmap='Reds')

	Model	Total Count	Percentage
0	MUSTANG	17	1.55%
1	CORVETTE	14	1.27%
2	RANGE ROVER	7	0.64%
3	CAYENNE	7	0.64%
4	ROADSTER	6	0.55%
5	CONTINENTAL	6	0.55%
6	BEL AIR	6	0.55%
7	X5	6	0.55%
8	A8	5	0.45%
9	COUPE	5	0.45%

fig = px.funnel(insight2,x="Model",y="Total Count",color="Model",text="Percentage",title = "Top 10 Car Models")
fig.show()

Insight 3 : Top 20 Car Make Years

c = dict(df['Year'].value_counts())
insight3 = pd.DataFrame()
c = dict(sorted(c.items(), key=operator.itemgetter(1), reverse=True)[:20])
insight3['Year'] = c.keys()
insight3['Total Count'] = c.values()
insight3['Percentage'] = ((insight3['Total Count'] / 1099)*100).round(2)
insight3['Percentage'] = insight3['Percentage'].astype(str) + "%"
insight3.style.background_gradient(cmap='Reds')

	Year	Total Count	Percentage
0	1989	69	6.28%
1	2015	46	4.19%
2	2010	40	3.64%
3	2013	39	3.55%
4	2014	35	3.18%
5	2016	35	3.18%
6	1970	33	3.0%
7	2012	31	2.82%
8	2017	29	2.64%
9	1968	28	2.55%
10	2008	26	2.37%
11	1971	25	2.27%
12	2011	25	2.27%
13	2007	25	2.27%
14	2009	22	2.0%
15	1967	22	2.0%
16	1975	20	1.82%
17	1969	20	1.82%
18	1965	19	1.73%
19	1974	19	1.73%

fig = px.sunburst(insight3, path=['Year', 'Total Count', 'Percentage'], values='Total Count',title="Top 20 Car Make Years")
fig.update_layout(margin = dict(t=0, l=0, r=0, b=0))
fig.show()

Insight 4 : Car Listing Types

d = dict(df['Listing Type'].value_counts())
insight4 = pd.DataFrame()
d = dict(sorted(d.items(), key=operator.itemgetter(1), reverse=True)[:2])
insight4['Listing Type'] = d.keys()
insight4['Total Count'] = d.values()
insight4['Percentage'] = ((insight4['Total Count'] / 1099)*100).round(2)
insight4['Listing Type'] = insight4['Listing Type'].replace(r'nan', "Not available", regex=True)
insight4['Percentage'] = insight4['Percentage'].astype(str) + "%"
insight4.style.background_gradient(cmap='Reds')

	Listing Type	Total Count	Percentage
0	Used	1041	94.72%
1	Not available	54	4.91%

fig = px.bar(insight4, x='Listing Type',y='Total Count',text='Percentage',title="Car Listing Types",color='Listing Type')
fig.show()

Insight 5 : Car Body Types

e = dict(df['Body Type'].value_counts())
insight5 = pd.DataFrame()
e = dict(sorted(e.items(), key=operator.itemgetter(1), reverse=True)[:20])
insight5['Body Type'] = e.keys()
insight5['Total Count'] = e.values()
insight5['Percentage'] = ((insight5['Total Count'] / 1099)*100).round(2)
insight5['Percentage'] = insight5['Percentage'].astype(str) + "%"
insight5['Body Type'] = insight5['Body Type'].replace(r'nan', "Not available", regex=True)
insight5.style.background_gradient(cmap='Reds')

	Body Type	Total Count	Percentage
0	Sedan	289	26.3%
1	Hardtop - Coupe	250	22.75%
2	Wagon	115	10.46%
3	Convertible	112	10.19%
4	Ute	86	7.83%
5	Not available	54	4.91%
6	Roadster	51	4.64%
7	Hatchback	33	3.0%
8	Van	32	2.91%
9	Fastback	18	1.64%
10	Panel Van	12	1.09%
11	Cab Chassis	8	0.73%
12	Soft Top	7	0.64%
13	Targa	7	0.64%
14	Limousine	7	0.64%
15	Open Wheeler	6	0.55%
16	Tourer	5	0.45%
17	Tray	4	0.36%
18	Style Side	3	0.27%

fig = px.bar(insight5, x="Body Type", y="Total Count",text = "Percentage",
             title ="Car Body Types",color = "Body Type")
fig.show()

Insight 6 : Car Fuel Types

f = dict(df['Fuel Type'].value_counts())
insight6 = pd.DataFrame()
f = dict(sorted(f.items(), key=operator.itemgetter(1), reverse=True)[:10])
insight6['Fuel Type'] = f.keys()
insight6['Total Count'] = f.values()
insight6['Percentage'] = ((insight6['Total Count'] / 1099)*100).round(2)
insight6['Percentage'] = insight6['Percentage'].astype(str) + "%"
insight6['Fuel Type'] = insight6['Fuel Type'].replace(r'nan', "Not available", regex=True)
insight6.style.background_gradient(cmap='Reds')

	Fuel Type	Total Count	Percentage
0	Not available	415	37.76%
1	Unleaded	253	23.02%
2	Premium Unleaded	244	22.2%
3	Diesel	106	9.65%
4	Leaded Petrol	66	6.01%
5	Dual Fuel	6	0.55%
6	LPG	6	0.55%
7	Ethanol	2	0.18%
8	Hybrid	1	0.09%

fig = px.bar(insight6, x="Fuel Type", y="Total Count",text = "Percentage",
             title ="Car Fuel Type",color = "Fuel Type")
fig.show()

Insight 7 : Top 10 Car Engine Capacity

g = dict(df['Engine Capacity - Cc'].value_counts())
insight7 = pd.DataFrame()
g = dict(sorted(g.items(), key=operator.itemgetter(1), reverse=True)[:10])
insight7['Engine Capacity - Cc'] = g.keys()
insight7['Total Count'] = g.values()
insight7['Percentage'] = ((insight7['Total Count'] / 1099)*100).round(2)
insight7['Percentage'] = insight7['Percentage'].astype(str) + "%"
insight7['Engine Capacity - Cc'] = insight7['Engine Capacity - Cc'].round(2)
insight7.style.background_gradient(cmap='Reds')

	Engine Capacity - Cc	Total Count	Percentage
0	1989	585	53.23%
1	0	25	2.27%
2	2000	21	1.91%
3	350	16	1.46%
4	1600	15	1.36%
5	5000	14	1.27%
6	2993	12	1.09%
7	3000	11	1.0%
8	4200	10	0.91%
9	3200	9	0.82%

fig = px.funnel(insight7, x="Engine Capacity - Cc", y="Total Count",text = "Percentage",
             title ="Top 10 Car Engine Capacity - Cc",color = "Total Count")
fig.show()

Insight 8 : Top 10 Engine Power - KW

h = dict(df['Engine Power - KW'].value_counts())
insight8 = pd.DataFrame()
h = dict(sorted(h.items(), key=operator.itemgetter(1), reverse=True)[:10])
insight8['Engine Power - KW'] = h.keys()
insight8['Total Count'] = h.values()
insight8['Percentage'] = ((insight8['Total Count'] / 1099)*100).round(2)
insight8['Engine Power - KW'] = insight8['Engine Power - KW'].replace(r'nan', "Not available", regex=True)
insight8['Percentage'] = insight8['Percentage'].astype(str) + "%"
insight8.style.background_gradient(cmap='Reds')

	Engine Power - KW	Total Count	Percentage
0	Not available	944	85.9%
1	0	13	1.18%
2	200	5	0.45%
3	350	4	0.36%
4	450	3	0.27%
5	290	3	0.27%
6	45	3	0.27%
7	190	3	0.27%
8	149	3	0.27%
9	330	2	0.18%

fig = px.bar(insight8, x="Engine Power - KW", y="Total Count",text = "Percentage",
             title ="Top 10 Car Engine Power - KW",color = "Total Count")
fig.show()

Insight 9 : Top 10 Car Prices

df['Price'] = df['Price'].mask(df.Price < 0, 0)

i = dict(df['Price'].value_counts())
insight9 = pd.DataFrame()
i = dict(sorted(i.items(), key=operator.itemgetter(1), reverse=True)[:10])
insight9['Price'] = i.keys()
insight9['Total Count'] = i.values()
insight9['Percentage'] = ((insight9['Total Count'] / 1099)*100).round(2)
insight9['Percentage'] = insight9['Percentage'].astype(str) + "%"
insight9.style.background_gradient(cmap='Reds')

	Price	Total Count	Percentage
0	7922666	74	6.73%
1	39990	25	2.27%
2	59990	22	2.0%
3	49990	20	1.82%
4	25000	17	1.55%
5	19990	15	1.36%
6	29990	14	1.27%
7	34990	14	1.27%
8	14990	14	1.27%
9	45000	13	1.18%

fig = px.sunburst(insight9, path = ['Price','Total Count','Percentage'],
             title ="Top 10 Car Price",color = "Total Count")
fig.show()

Insight 10: Top 7 Car Makes according to Model

df['COUNTER'] = 1
insight10 = df.groupby(['Make','Model'])['COUNTER'].agg(['count'])
insight10['Percentage'] = ((insight10['count'] / 1099)*100).round(2) 
insight10['Percentage'] = insight10['Percentage'].astype(str) + '%'
insight10 = insight10.reset_index(['Make','Model'])
insight10= insight10.nlargest(7,'count')
insight10.reset_index(inplace=True,drop=True)

insight10.style.background_gradient(cmap='Reds')

	Make	Model	count	Percentage
0	FORD	MUSTANG	17	1.55%
1	CHEVROLET	CORVETTE	14	1.27%
2	LAND ROVER	RANGE ROVER	7	0.64%
3	PORSCHE	CAYENNE	7	0.64%
4	BMW	X5	6	0.55%
5	CHEVROLET	BEL AIR	6	0.55%
6	AUDI	A8	5	0.45%

fig = px.bar(insight10,x="Make", y="count", color="Model",barmode='group', text="Percentage", 
             title = "Top 7 Car Makes according to Model")
fig.show()

Insight 11: Top 10 Car Makes according to Year

df['COUNTER'] = 1
insight11 = df.groupby(['Make','Year'])['COUNTER'].agg(['count'])
insight11['Percentage'] = ((insight11['count'] / 1099)*100).round(2) 
insight11['Percentage'] = insight11['Percentage'].astype(str) + '%'
insight11 = insight11.reset_index(['Make','Year'])
insight11 = insight11.nlargest(10,'count')
insight11.reset_index(inplace=True,drop=True)

insight11.style.background_gradient(cmap='Reds')

	Make	Year	count	Percentage
0	MERCEDES-BENZ	2015	14	1.27%
1	FORD	1971	13	1.18%
2	FORD	1970	12	1.09%
3	MERCEDES-BENZ	2013	12	1.09%
4	FORD	1989	11	1.0%
5	MERCEDES-BENZ	2010	10	0.91%
6	CHEVROLET	1968	9	0.82%
7	FORD	1968	9	0.82%
8	FORD	1973	9	0.82%
9	HOLDEN	1975	9	0.82%

fig = px.bar(insight11,x="Make", y="count", color="Year",barmode='group',text = "Percentage",
             title = "Top 10 Car Makes according to Year")
fig.show()

Insight 12: Top 10 Car Makes according to Transmission

df['COUNTER'] = 1
insight12 = df.groupby(['Make','Transmission'])['COUNTER'].agg(['count'])
insight12['Percentage'] = ((insight12['count'] / 1099)*100).round(2) 
insight12['Percentage'] = insight12['Percentage'].astype(str) + '%'
insight12 = insight12.reset_index(['Make','Transmission'])
insight12['Transmission'] = insight12['Transmission'].replace(r'nan', "Not available", regex=True)
insight12 = insight12.nlargest(10,'count')
insight12.reset_index(inplace=True,drop=True)

insight12.style.background_gradient(cmap='Reds')

	Make	Transmission	count	Percentage
0	FORD	Automatic	92	8.37%
1	MERCEDES-BENZ	Automatic	80	7.28%
2	HOLDEN	Automatic	49	4.46%
3	FORD	Manual	46	4.19%
4	FORD	Not available	41	3.73%
5	CHEVROLET	Automatic	37	3.37%
6	MERCEDES-BENZ	Not available	35	3.18%
7	HOLDEN	Manual	29	2.64%
8	CHEVROLET	Manual	26	2.37%
9	BMW	Not available	24	2.18%

fig = px.bar(insight12,x="Make", y="count", color="Transmission",barmode='group',text = "Percentage",
             title = "Top 10 Car Makes according to Transmission")
fig.show()

Insight 13: Top 10 Car Makes according to Listing Type

df['COUNTER'] = 1
insight13 = df.groupby(['Make','Listing Type'])['COUNTER'].agg(['count'])
insight13['Percentage'] = ((insight13['count'] / 1099)*100).round(2) 
insight13['Percentage'] = insight13['Percentage'].astype(str) + '%'
insight13 = insight13.reset_index(['Make','Listing Type'])
insight13 = insight13.nlargest(10,'count')
insight13.reset_index(inplace=True,drop=True)

insight13.style.background_gradient(cmap='Reds')

	Make	Listing Type	count	Percentage
0	FORD	Used	180	16.38%
1	MERCEDES-BENZ	Used	118	10.74%
2	HOLDEN	Used	103	9.37%
3	CHEVROLET	Used	76	6.92%
4	BMW	Used	46	4.19%
5	JAGUAR	Used	39	3.55%
6	HSV	Used	30	2.73%
7	PORSCHE	Used	30	2.73%
8	VOLKSWAGEN	Used	30	2.73%
9	AUDI	Used	27	2.46%

fig = px.bar(insight13,x="Make", y="count", color="Listing Type",barmode='group',text = "Percentage",
             title = "Top 10 Car Makes according to Listing Type")
fig.show()

Insight 14: Top 10 Car Makes according to Body Type

df['COUNTER'] = 1
insight14 = df.groupby(['Make','Body Type'])['COUNTER'].agg(['count'])
insight14['Percentage'] = ((insight14['count'] / 1099)*100).round(2) 
insight14['Percentage'] = insight14['Percentage'].astype(str) + '%'
insight14 = insight14.reset_index(['Make','Body Type'])
insight14 = insight14.nlargest(10,'count')
insight14.reset_index(inplace=True,drop=True)

insight14.style.background_gradient(cmap='Reds')

	Make	Body Type	count	Percentage
0	HOLDEN	Sedan	53	4.82%
1	FORD	Sedan	44	4.0%
2	MERCEDES-BENZ	Sedan	44	4.0%
3	FORD	Hardtop - Coupe	41	3.73%
4	FORD	Ute	37	3.37%
5	CHEVROLET	Hardtop - Coupe	35	3.18%
6	MERCEDES-BENZ	Hardtop - Coupe	27	2.46%
7	HSV	Sedan	24	2.18%
8	JAGUAR	Sedan	20	1.82%
9	HOLDEN	Hardtop - Coupe	16	1.46%

fig = px.bar(insight14,x="Make", y="count", color="Body Type",barmode='group',text = "Percentage",
             title = "Top 10 Car Makes according to Body Type")
fig.show()

Insight 15: Top 10 Car Makes according to Fuel Type

df['COUNTER'] = 1
insight15 = df.groupby(['Make','Fuel Type'])['COUNTER'].agg(['count'])
insight15['Percentage'] = ((insight15['count'] / 1099)*100).round(2) 
insight15['Percentage'] = insight15['Percentage'].astype(str) + '%'
insight15 = insight15.reset_index(['Make','Fuel Type'])
insight15['Fuel Type'] = insight15['Fuel Type'].replace(r'nan', "Not available", regex=True)
insight15 = insight15.nlargest(10,'count')
insight15.reset_index(inplace=True,drop=True)

insight15.style.background_gradient(cmap='Reds')

	Make	Fuel Type	count	Percentage
0	FORD	Unleaded	61	5.55%
1	FORD	Not available	59	5.37%
2	HOLDEN	Not available	38	3.46%
3	MERCEDES-BENZ	Not available	38	3.46%
4	MERCEDES-BENZ	Premium Unleaded	35	3.18%
5	CHEVROLET	Unleaded	33	3.0%
6	FORD	Premium Unleaded	33	3.0%
7	HOLDEN	Premium Unleaded	31	2.82%
8	BMW	Not available	24	2.18%
9	MERCEDES-BENZ	Unleaded	21	1.91%

fig = px.bar(insight15,x="Make", y="count", color="Fuel Type",barmode='group',text = "Percentage",
             title = "Top 10 Car Makes according to Fuel Type")
fig.show()

Insight 16: Top 10 Car Makes according to Engine Capacity - Cc

df['COUNTER'] = 1
insight16 = df.groupby(['Make','Engine Capacity - Cc'])['COUNTER'].agg(['count'])
insight16['Percentage'] = ((insight16['count'] / 1099)*100).round(2) 
insight16['Percentage'] = insight16['Percentage'].astype(str) + '%'
insight16 = insight16.reset_index(['Make','Engine Capacity - Cc'])
insight16 = insight16.nlargest(10,'count')
insight16.reset_index(inplace=True,drop=True)

insight16.style.background_gradient(cmap='Reds')

	Make	Engine Capacity - Cc	count	Percentage
0	FORD	1989	107	9.74%
1	HOLDEN	1989	70	6.37%
2	MERCEDES-BENZ	1989	54	4.91%
3	CHEVROLET	1989	48	4.37%
4	BMW	1989	25	2.27%
5	HSV	1989	23	2.09%
6	CHRYSLER	1989	16	1.46%
7	JAGUAR	1989	16	1.46%
8	PONTIAC	1989	16	1.46%
9	AUDI	1989	12	1.09%

fig = px.bar(insight16,x="Make", y="count", color="Engine Capacity - Cc",barmode='group',text = "Percentage",
             title = "Top 10 Car Makes according to Engine Capacity - Cc")
fig.show()

Insight 17: Top 10 Car Makes according to Engine Power - KW

df['COUNTER'] = 1
insight17 = df.groupby(['Make','Engine Power - KW'])['COUNTER'].agg(['count'])
insight17['Percentage'] = ((insight17['count'] / 1099)*100).round(2) 
insight17['Percentage'] = insight17['Percentage'].astype(str) + '%'
insight17 = insight17.reset_index(['Make','Engine Power - KW'])
insight17['Engine Power - KW'] = insight17['Engine Power - KW'].replace(r'nan', "Not available", regex=True)
insight17 = insight17.nlargest(10,'count')
insight17.reset_index(inplace=True,drop=True)

insight17.style.background_gradient(cmap='Reds')

	Make	Engine Power - KW	count	Percentage
0	FORD	Not available	145	13.19%
1	MERCEDES-BENZ	Not available	112	10.19%
2	HOLDEN	Not available	73	6.64%
3	CHEVROLET	Not available	65	5.91%
4	BMW	Not available	49	4.46%
5	JAGUAR	Not available	37	3.37%
6	VOLKSWAGEN	Not available	28	2.55%
7	HSV	Not available	27	2.46%
8	PORSCHE	Not available	27	2.46%
9	AUDI	Not available	23	2.09%

fig = px.bar(insight17,x="Make", y="count",barmode='group',text = "Percentage",
             title = "Top 10 Car Makes according to Engine Power - KW")
fig.show()

Insight 18: Top 10 Car Makes according to Price

df['COUNTER'] = 1
insight18 = df.groupby(['Make','Price'])['COUNTER'].agg(['count'])
insight18['Percentage'] = ((insight18['count'] / 1099)*100).round(2) 
insight18['Percentage'] = insight18['Percentage'].astype(str) + '%'
insight18 = insight18.reset_index(['Make','Price'])
insight18 = insight18.nlargest(10,'count')
insight18.reset_index(inplace=True,drop=True)

insight18.style.background_gradient(cmap='Reds')

	Make	Price	count	Percentage
0	FORD	7922666	13	1.18%
1	HOLDEN	7922666	12	1.09%
2	MERCEDES-BENZ	49990	8	0.73%
3	MERCEDES-BENZ	29990	7	0.64%
4	MERCEDES-BENZ	39990	7	0.64%
5	AUDI	39990	6	0.55%
6	CHEVROLET	7922666	6	0.55%
7	FORD	95000	6	0.55%
8	MERCEDES-BENZ	26990	6	0.55%
9	FORD	59990	5	0.45%

fig = px.bar(insight18,x="Make", y="count", color="Price",barmode='group',text = "Percentage",
             title = "Top 10 Car Makes according to Price")
fig.show()

Insight 19: Top 10 Car Model according to Transmission

df['COUNTER'] = 1
insight19 = df.groupby(['Model','Transmission'])['COUNTER'].agg(['count'])
insight19['Percentage'] = ((insight19['count'] / 1099)*100).round(2) 
insight19['Percentage'] = insight19['Percentage'].astype(str) + '%'
insight19 = insight19.reset_index(['Model','Transmission'])
insight19 = insight19.nlargest(10,'count')
insight19.reset_index(inplace=True,drop=True)

insight19.style.background_gradient(cmap='Reds')

	Model	Transmission	count	Percentage
0	MUSTANG	Manual	8	0.73%
1	RANGE ROVER	Automatic	7	0.64%
2	CONTINENTAL	Automatic	6	0.55%
3	CORVETTE	Manual	6	0.55%
4	MUSTANG	Automatic	6	0.55%
5	X5	Automatic	6	0.55%
6	A8	Tiptronic	5	0.45%
7	CAYENNE	Tiptronic	5	0.45%
8	CORVETTE	Automatic	5	0.45%
9	ROADSTER	Automatic	5	0.45%

fig = px.bar(insight19,x="Model", y="count", color="Transmission",barmode='group',text = "Percentage",
             title = "Top 10 Car Models according to Transmission")
fig.show()

Insight 20: Top 10 Car Model according to Listing Type

df['COUNTER'] = 1
insight20 = df.groupby(['Model','Listing Type'])['COUNTER'].agg(['count'])
insight20['Percentage'] = ((insight20['count'] / 1099)*100).round(2) 
insight20['Percentage'] = insight20['Percentage'].astype(str) + '%'
insight20 = insight20.reset_index(['Model','Listing Type'])
insight20 = insight20.nlargest(10,'count')
insight20.reset_index(inplace=True,drop=True)

insight20.style.background_gradient(cmap='Reds')

	Model	Listing Type	count	Percentage
0	MUSTANG	Used	17	1.55%
1	CORVETTE	Used	14	1.27%
2	CAYENNE	Used	7	0.64%
3	RANGE ROVER	Used	7	0.64%
4	BEL AIR	Used	6	0.55%
5	CONTINENTAL	Used	6	0.55%
6	ROADSTER	Used	6	0.55%
7	X5	Used	6	0.55%
8	A8	Used	5	0.45%
9	BONNEVILLE	Used	5	0.45%

fig = px.bar(insight20,x="Model", y="count", color="Listing Type",barmode='group',text = "Percentage",
             title = "Top 10 Car Models according to Listing Type")
fig.show()

Insight 21: Top 10 Car Models according to Body Type

df['COUNTER'] = 1
insight21 = df.groupby(['Model','Body Type'])['COUNTER'].agg(['count'])
insight21['Percentage'] = ((insight21['count'] / 1099)*100).round(2) 
insight21['Percentage'] = insight21['Percentage'].astype(str) + '%'
insight21 = insight21.reset_index(['Model','Body Type'])
insight21 = insight21.nlargest(10,'count')
insight21.reset_index(inplace=True,drop=True)

insight21.style.background_gradient(cmap='Reds')

	Model	Body Type	count	Percentage
0	MUSTANG	Hardtop - Coupe	12	1.09%
1	CAYENNE	Wagon	7	0.64%
2	RANGE ROVER	Wagon	7	0.64%
3	BEL AIR	Hardtop - Coupe	6	0.55%
4	CORVETTE	Hardtop - Coupe	6	0.55%
5	ROADSTER	Roadster	6	0.55%
6	X5	Wagon	6	0.55%
7	A8	Sedan	5	0.45%
8	3000 MK III BJ8	Convertible	4	0.36%
9	CORVETTE	Roadster	4	0.36%

fig = px.bar(insight21,x="Model", y="count", color="Body Type",barmode='group',text = "Percentage",
             title = "Top 10 Car Models according to Body Type")
fig.show()

Insight 22: Top 10 Car Model according to Fuel Type

df['COUNTER'] = 1
insight22 = df.groupby(['Model','Fuel Type'])['COUNTER'].agg(['count'])
insight22['Percentage'] = ((insight22['count'] / 1099)*100).round(2) 
insight22['Percentage'] = insight22['Percentage'].astype(str) + '%'
insight22 = insight22.reset_index(['Model','Fuel Type'])
insight22['Fuel Type'] = insight22['Fuel Type'].replace(r'nan', "Not available", regex=True)
insight22 = insight22.nlargest(10,'count')
insight22.reset_index(inplace=True,drop=True)

insight22.style.background_gradient(cmap='Reds')

	Model	Fuel Type	count	Percentage
0	MUSTANG	Not available	8	0.73%
1	RANGE ROVER	Diesel	7	0.64%
2	CONTINENTAL	Premium Unleaded	5	0.45%
3	MUSTANG	Unleaded	5	0.45%
4	X5	Diesel	5	0.45%
5	3000 MK III BJ8	Leaded Petrol	4	0.36%
6	450SL	Unleaded	4	0.36%
7	560	Unleaded	4	0.36%
8	C200	Premium Unleaded	4	0.36%
9	C250	Premium Unleaded	4	0.36%

fig = px.bar(insight22,x="Model", y="count", color="Fuel Type",barmode='group',text = "Percentage",
             title = "Top 10 Car Models according to Fuel Type")
fig.show()

Insight 23: Top 10 Car Model according to Engine Capacity - Cc

df['COUNTER'] = 1
insight23 = df.groupby(['Model','Engine Capacity - Cc'])['COUNTER'].agg(['count'])
insight23['Percentage'] = ((insight23['count'] / 1099)*100).round(2) 
insight23['Percentage'] = insight23['Percentage'].astype(str) + '%'
insight23 = insight23.reset_index(['Model','Engine Capacity - Cc'])
insight23 = insight23.nlargest(10,'count')
insight23.reset_index(inplace=True,drop=True)

insight23.style.background_gradient(cmap='Reds')

	Model	Engine Capacity - Cc	count	Percentage
0	CORVETTE	1989	9	0.82%
1	MUSTANG	1989	9	0.82%
2	BONNEVILLE	1989	5	0.45%
3	MUSTANG	0	5	0.45%
4	ROADSTER	1989	5	0.45%
5	3000 MK III BJ8	2912	4	0.36%
6	450SL	1989	4	0.36%
7	560	5547	4	0.36%
8	98	1989	4	0.36%
9	BEL AIR	1989	4	0.36%

fig = px.bar(insight23,x="Model", y="count", color="Engine Capacity - Cc",barmode='group',text = "Percentage",
             title = "Top 10 Car Models according to Engine Capacity - Cc")
fig.show()

Insight 24: Top 10 Car Model according to Engine Power - KW

df['COUNTER'] = 1
insight24 = df.groupby(['Model','Engine Power - KW'])['COUNTER'].agg(['count'])
insight24['Percentage'] = ((insight24['count'] / 1099)*100).round(2) 
insight24['Percentage'] = insight24['Percentage'].astype(str) + '%'
insight24 = insight24.reset_index(['Model','Engine Power - KW'])
insight24['Engine Power - KW'] = insight24['Engine Power - KW'].replace(r'nan', "Not available", regex=True)
insight24 = insight24.nlargest(10,'count')
insight24.reset_index(inplace=True,drop=True)

insight24.style.background_gradient(cmap='Reds')

	Model	Engine Power - KW	count	Percentage
0	MUSTANG	Not available	15	1.36%
1	CORVETTE	Not available	12	1.09%
2	CAYENNE	Not available	7	0.64%
3	RANGE ROVER	Not available	7	0.64%
4	CONTINENTAL	Not available	6	0.55%
5	ROADSTER	Not available	6	0.55%
6	X5	Not available	6	0.55%
7	A8	Not available	5	0.45%
8	BONNEVILLE	Not available	5	0.45%
9	3000 MK III BJ8	Not available	4	0.36%

fig = px.bar(insight24,x="Model", y="count", barmode='group',text = "Percentage",
             title = "Top 10 Car Models according to Engine Power - KW")
fig.show()

Insight 25: Top 10 Car Model according to Year

df['COUNTER'] = 1
insight25 = df.groupby(['Model','Year'])['COUNTER'].agg(['count'])
insight25['Percentage'] = ((insight25['count'] / 1099)*100).round(2) 
insight25['Percentage'] = insight25['Percentage'].astype(str) + '%'
insight25 = insight25.reset_index(['Model','Year'])
insight25 = insight25.nlargest(10,'count')
insight25.reset_index(inplace=True,drop=True)

insight25.style.background_gradient(cmap='Reds')

	Model	Year	count	Percentage
0	BEL AIR	1955	4	0.36%
1	FAIRLANE ZD	1971	4	0.36%
2	ROADSTER	1989	4	0.36%
3	X5	2014	4	0.36%
4	COUPE Master 85	1939	3	0.27%
5	308 GTB Vetroresina	1976	3	0.27%
6	BONNEVILLE	1963	3	0.27%
7	C-CLASS W204 MY13	2012	3	0.27%
8	CAMARO	1968	3	0.27%
9	CHALLENGER	1970	3	0.27%

fig = px.bar(insight25,x="Model", y="count", color="Year",barmode='group',text = "Percentage",
             title = "Top 10 Car Models according to Year")
fig.show()

Insight 26: Top 10 Car Model according to Price

df['COUNTER'] = 1
insight26 = df.groupby(['Model','Price'])['COUNTER'].agg(['count'])
insight26['Percentage'] = ((insight26['count'] / 1099)*100).round(2) 
insight26['Percentage'] = insight26['Percentage'].astype(str) + '%'
insight26 = insight26.reset_index(['Model','Price'])
insight26 = insight26.nlargest(10,'count')
insight26.reset_index(inplace=True,drop=True)

insight26.style.background_gradient(cmap='Reds')

	Model	Price	count	Percentage
0	A8	39990	4	0.36%
1	FAIRLANE ZD	82500	4	0.36%
2	ROADSTER	40000	4	0.36%
3	COUPE Master 85	28000	3	0.27%
4	C-CLASS W204 MY13	26990	3	0.27%
5	HX GTS Clone	50000	3	0.27%
6	MUSTANG	45000	3	0.27%
7	MUSTANG	129990	3	0.27%
8	TORANA LX	7922666	3	0.27%
9	X7	65000	2	0.18%

fig = px.bar(insight26,x="Model", y="count", color="Price",barmode='group',text = "Percentage",
             title = "Top 10 Car Models according to Price")
fig.show()

Insight 27: Top 10 Body Type according to Price

df['COUNTER'] = 1
insight27 = df.groupby(['Body Type','Price'])['COUNTER'].agg(['count'])
insight27['Percentage'] = ((insight27['count'] / 1099)*100).round(2) 
insight27['Percentage'] = insight27['Percentage'].astype(str) + '%'
insight27 = insight27.reset_index(['Body Type','Price'])
insight27 = insight27.nlargest(10,'count')
insight27.reset_index(inplace=True,drop=True)

insight27.style.background_gradient(cmap='Reds')

	Body Type	Price	count	Percentage
0	Hardtop - Coupe	7922666	24	2.18%
1	nan	7922666	15	1.36%
2	Sedan	39990	14	1.27%
3	Sedan	7922666	14	1.27%
4	Sedan	25000	9	0.82%
5	Hardtop - Coupe	129990	8	0.73%
6	Sedan	49990	8	0.73%
7	Convertible	7922666	6	0.55%
8	Hardtop - Coupe	95000	6	0.55%
9	Sedan	20000	6	0.55%

fig = px.bar(insight27,x="Body Type", y="count", color="Price",barmode='group',text = "Percentage",
             title = "Top 10 Car Body Type according to Price")
fig.show()

Insight 28: Top 10 Fuel Type according to Price

df['COUNTER'] = 1
insight28 = df.groupby(['Fuel Type','Price'])['COUNTER'].agg(['count'])
insight28['Percentage'] = ((insight28['count'] / 1099)*100).round(2) 
insight28['Percentage'] = insight28['Percentage'].astype(str) + '%'
insight28 = insight28.reset_index(['Fuel Type','Price'])
insight28['Fuel Type'] = insight28['Fuel Type'].replace(r'nan', "Not available", regex=True)
insight28 = insight28.nlargest(10,'count')
insight28.reset_index(inplace=True,drop=True)

insight28.style.background_gradient(cmap='Reds')

	Fuel Type	Price	count	Percentage
0	Not available	7922666	70	6.37%
1	Not available	14990	10	0.91%
2	Not available	49990	10	0.91%
3	Diesel	29990	8	0.73%
4	Premium Unleaded	25000	8	0.73%
5	Premium Unleaded	95000	8	0.73%
6	Unleaded	59990	8	0.73%
7	Not available	39990	8	0.73%
8	Diesel	39990	7	0.64%
9	Unleaded	35000	7	0.64%

fig = px.bar(insight28,x="Fuel Type", y="count", color="Price",barmode='group',text = "Percentage",
             title = "Top 10 Car Fuel Type according to Price")
fig.show()

Insight 29: Top 5 Listing Type according to Price

df['COUNTER'] = 1
insight29 = df.groupby(['Listing Type','Price'])['COUNTER'].agg(['count'])
insight29['Percentage'] = ((insight29['count'] / 1099)*100).round(2) 
insight29['Percentage'] = insight29['Percentage'].astype(str) + '%'
insight29 = insight29.reset_index(['Listing Type','Price'])
insight29 = insight29.nlargest(5,'count')
insight29.reset_index(inplace=True,drop=True)

insight29.style.background_gradient(cmap='Reds')

	Listing Type	Price	count	Percentage
0	Used	7922666	59	5.37%
1	Used	39990	25	2.27%
2	Used	59990	22	2.0%
3	Used	49990	20	1.82%
4	Used	25000	17	1.55%

fig = px.bar(insight29,x="Listing Type", y="count", color="Price",barmode='group',text = "Percentage",
             title = "Top 5 Car Listing Type according to Price")
fig.show()

Insight 30: Top 10 Transmission according to Price

df['COUNTER'] = 1
insight30 = df.groupby(['Transmission','Price'])['COUNTER'].agg(['count'])
insight30['Percentage'] = ((insight30['count'] / 1099)*100).round(2) 
insight30['Percentage'] = insight30['Percentage'].astype(str) + '%'
insight30 = insight30.reset_index(['Transmission','Price'])
insight30['Transmission'] = insight30['Transmission'].replace(r'nan', "Not available", regex=True)
insight30 = insight30.nlargest(10,'count')
insight30.reset_index(inplace=True,drop=True)

insight30.style.background_gradient(cmap='Reds')

	Transmission	Price	count	Percentage
0	Not available	7922666	31	2.82%
1	Automatic	7922666	25	2.27%
2	Manual	7922666	18	1.64%
3	Automatic	59990	10	0.91%
4	Not available	14990	10	0.91%
5	Not available	49990	10	0.91%
6	Automatic	29990	9	0.82%
7	Automatic	39990	9	0.82%
8	Automatic	49990	9	0.82%
9	Automatic	69990	8	0.73%

fig = px.bar(insight30,x="Transmission", y="count", color="Price",barmode='group',text = "Percentage",
             title = "Top 10 Transmission according to Price")
fig.show()

Insight 31: Top 10 Engine Capacity according to Price

df['COUNTER'] = 1
insight31 = df.groupby(['Engine Capacity - Cc','Price'])['COUNTER'].agg(['count'])
insight31['Percentage'] = ((insight31['count'] / 1099)*100).round(2) 
insight31['Percentage'] = insight31['Percentage'].astype(str) + '%'
insight31 = insight31.reset_index(['Engine Capacity - Cc','Price'])
insight31 = insight31.nlargest(10,'count')
insight31.reset_index(inplace=True,drop=True)

insight31.style.background_gradient(cmap='Reds')

	Engine Capacity - Cc	Price	count	Percentage
0	1989	7922666	70	6.37%
1	1989	39990	12	1.09%
2	1989	59990	12	1.09%
3	1989	49990	11	1.0%
4	1989	14990	10	0.91%
5	1989	40000	9	0.82%
6	1989	45000	9	0.82%
7	1989	25000	8	0.73%
8	1989	19990	7	0.64%
9	1989	26990	7	0.64%

fig = px.bar(insight31,x="Engine Capacity - Cc", y="count", color="Price",barmode='group',text = "Percentage",
             title = "Top 10 Car Engine Capacity - Cc according to Price ")
fig.show()

Insight 32: Top 10 Transmission according to Body Type

df['COUNTER'] = 1
insight32 = df.groupby(['Transmission','Body Type'])['COUNTER'].agg(['count'])
insight32['Percentage'] = ((insight32['count'] / 1099)*100).round(2) 
insight32['Percentage'] = insight32['Percentage'].astype(str) + '%'
insight32 = insight32.reset_index(['Transmission','Body Type'])
insight32['Transmission'] = insight32['Transmission'].replace(r'nan', "Not available", regex=True)
insight32['Body Type'] = insight32['Body Type'].replace(r'nan', "Not available", regex=True)
insight32 = insight32.nlargest(10,'count')
insight32.reset_index(inplace=True,drop=True)

insight32.style.background_gradient(cmap='Reds')

	Transmission	Body Type	count	Percentage
0	Automatic	Sedan	128	11.65%
1	Automatic	Hardtop - Coupe	114	10.37%
2	Not available	Sedan	90	8.19%
3	Automatic	Wagon	75	6.82%
4	Manual	Hardtop - Coupe	68	6.19%
5	Not available	Hardtop - Coupe	61	5.55%
6	Manual	Sedan	56	5.1%
7	Not available	Not available	54	4.91%
8	Automatic	Ute	50	4.55%
9	Manual	Convertible	40	3.64%

fig = px.bar(insight32,x="Transmission", y="count", color="Body Type",barmode='group',text = "Percentage",
             title = "Top 10 Transmission according to Body Type")
fig.show()

Insight 33: Top 10 Fuel Type according to Body Type

df['COUNTER'] = 1
insight33 = df.groupby(['Fuel Type','Body Type'])['COUNTER'].agg(['count'])
insight33['Percentage'] = ((insight33['count'] / 1099)*100).round(2) 
insight33['Percentage'] = insight33['Percentage'].astype(str) + '%'
insight33 = insight33.reset_index(['Fuel Type','Body Type'])
insight33['Fuel Type'] = insight33['Fuel Type'].replace(r'nan', "Not available", regex=True)
insight33['Body Type'] = insight33['Body Type'].replace(r'nan', "Not available", regex=True)
insight33 = insight33.nlargest(10,'count')
insight33.reset_index(inplace=True,drop=True)

insight33.style.background_gradient(cmap='Reds')

	Fuel Type	Body Type	count	Percentage
0	Not available	Hardtop - Coupe	112	10.19%
1	Not available	Sedan	112	10.19%
2	Premium Unleaded	Hardtop - Coupe	70	6.37%
3	Premium Unleaded	Sedan	68	6.19%
4	Unleaded	Sedan	62	5.64%
5	Not available	Not available	54	4.91%
6	Unleaded	Hardtop - Coupe	46	4.19%
7	Unleaded	Convertible	43	3.91%
8	Diesel	Wagon	41	3.73%
9	Not available	Convertible	35	3.18%

fig = px.bar(insight33,x="Fuel Type", y="count", color="Body Type",barmode='group',text = "Percentage",
             title = "Top 10 Fuel Type according to Body Type")
fig.show()

Insight 34: Top 20 Car Makes according to Car Model and Make Year

insight34 = df.groupby(['Make', 'Model', 'Year']).size().unstack(level=2, fill_value=0)

insight34 = (insight34.stack().reset_index(name='Total Count'))

insight34['Percentage'] = ((insight34['Total Count'] / 1099)*100).round(2)
insight34['Percentage'] = insight34['Percentage'].astype(str) + '%'
insight_34 = insight34.nlargest(20,'Total Count')

pd.crosstab([insight34['Make'], insight34['Total Count']],[insight34['Year']],margins=True)

	Year	1920	1922	1923	1926	1929	1931	1932	1934	1935	1936	...	2013	2014	2015	2016	2017	2018	2019	2020	2021	All
Make	Total Count
AC	0	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	85
	2	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	2
ALFA ROMEO	0	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	86
	2	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
ASTON MARTIN	0	6	6	6	6	6	6	6	6	6	6	...	5	6	6	6	6	6	6	6	5	516
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
WESTFIELD	0	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	86
	1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
nan	0	3	3	3	3	3	3	3	3	3	3	...	3	3	3	3	3	3	3	3	3	258
	1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	3
All		716	716	716	716	716	716	716	716	716	716	...	716	716	716	716	716	716	716	716	716	62292

249 rows × 88 columns

fig = px.bar(insight_34,x="Make", y="Total Count", color="Year",barmode='group',text="Percentage",
             title = "Top 20 Car Make according to Year")
fig.show()

pd.crosstab([insight34['Model'], insight34['Total Count']],[insight34['Year']],margins=True)

	Year	1920	1922	1923	1926	1929	1931	1932	1934	1935	1936	...	2013	2014	2015	2016	2017	2018	2019	2020	2021	All
Model	Total Count
COUPE Master 85	0	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	86
	3	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
X7	0	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	86
	2	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
1 SERIES E82 LCI MY13	0	1	1	1	1	1	1	1	1	1	1	...	0	1	1	1	1	1	1	1	1	86
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Cooper BRM F2	0	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	86
	1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
Jaguar E-Type 4.2 F H Coupe	0	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	86
	1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
All		716	716	716	716	716	716	716	716	716	716	...	716	716	716	716	716	716	716	716	716	62292

1454 rows × 88 columns

fig = px.bar(insight_34,x="Model", y="Total Count", color="Year",barmode='group',text="Percentage",
             title = "Top 20 Car Model according to Year")
fig.show()

Insight 35: Top 20 Car Makes according to Make Year and Price

insight35 = df.groupby(['Make', 'Year', 'Price']).size().unstack(level=2, fill_value=0)

insight35 = (insight35.stack().reset_index(name='Total Count'))

insight35['Percentage'] = ((insight35['Total Count'] / 1099)*100).round(2)
insight35['Percentage'] = insight35['Percentage'].astype(str) + '%'
insight_35 = insight35.nlargest(20,'Total Count')

pd.crosstab([insight35['Make'], insight35['Total Count']],[insight35['Year']],margins=True)

	Year	1920	1922	1923	1926	1929	1931	1932	1934	1935	1936	...	2013	2014	2015	2016	2017	2018	2019	2020	2021	All
Make	Total Count
AC	0	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	734
	2	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	2
ALFA ROMEO	0	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	367
	2	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
ASTON MARTIN	0	0	0	0	0	0	0	0	0	0	0	...	366	0	0	0	0	0	0	0	367	2201
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
WESTFIELD	0	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	367
	1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
nan	0	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1101
	1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	3
All		368	368	368	368	736	368	368	1104	368	736	...	5152	5152	6624	4048	4784	4416	2576	2208	1104	190624

246 rows × 88 columns

fig = px.bar(insight_35,x="Make", y="Total Count", color="Year",barmode='group',text="Percentage",
             title = "Top 20 Car Make according to Year")
fig.show()

pd.crosstab([insight35['Price'], insight35['Total Count']],[insight35['Year']],margins=True)

	Year	1920	1922	1923	1926	1929	1931	1932	1934	1935	1936	...	2013	2014	2015	2016	2017	2018	2019	2020	2021	All
Price	Total Count
0	0	1	1	1	1	2	1	1	3	1	2	...	14	14	18	11	13	12	7	6	3	517
	1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
1950	0	1	1	1	1	2	1	1	3	1	2	...	14	14	18	11	13	12	7	6	3	517
	1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
2500	0	1	1	1	1	2	1	1	3	1	2	...	14	14	18	11	13	12	7	6	3	517
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
219999000	0	1	1	1	1	2	1	1	3	1	2	...	14	14	18	11	13	12	7	6	3	517
	1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
2000020000	0	1	1	1	1	2	1	1	3	1	2	...	14	14	18	11	13	12	7	6	3	517
	1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
All		368	368	368	368	736	368	368	1104	368	736	...	5152	5152	6624	4048	4784	4416	2576	2208	1104	190624

832 rows × 88 columns

fig = px.bar(insight_35,x="Year", y="Total Count", color="Price",barmode='group',text="Percentage",
             title = "Top 20 Car Price according to Year")
fig.show()

Insight 36: Top 10 Car Makes according to Car Model and Body Type

insight36 = df.groupby(['Make', 'Model', 'Body Type']).size().unstack(level=2, fill_value=0)

insight36 = (insight36.stack().reset_index(name='Total Count'))

insight36['Percentage'] = ((insight36['Total Count'] / 1099)*100).round(2)
insight36['Percentage'] = insight36['Percentage'].astype(str) + '%'
insight_36 = insight36.nlargest(10,'Total Count')

fig = px.bar(insight_36,x="Make", y="Total Count", color="Model",barmode='group',text="Percentage",
             title = "Top 10 Car Make according to Model")
fig.show()

pd.crosstab([insight36['Make'], insight36['Total Count']],[insight36['Body Type']],margins=True)

	Body Type	Cab Chassis	Convertible	Fastback	Hardtop - Coupe	Hatchback	Limousine	Open Wheeler	Panel Van	Roadster	Sedan	Soft Top	Style Side	Targa	Tourer	Tray	Ute	Van	Wagon	nan	All
Make	Total Count
AC	0	1	1	1	1	1	1	1	1	0	1	0	1	1	1	1	1	1	1	1	17
	2	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	2
ALFA ROMEO	0	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	18
	2	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1
ASTON MARTIN	0	6	6	6	0	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6	108
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
WESTFIELD	0	1	1	1	1	1	1	1	1	0	1	1	1	1	1	1	1	1	1	1	18
	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1
nan	0	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	0	54
	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	3
All		716	716	716	716	716	716	716	716	716	716	716	716	716	716	716	716	716	716	716	13604

261 rows × 20 columns

fig = px.bar(insight_36,x="Model", y="Total Count", color="Body Type",barmode='group',text="Percentage",
             title = "Top 10 Car Makes according to Body Type")
fig.show()

Insight 37: Top 10 Car Model according to Body Type and Price

insight37 = df.groupby(['Model', 'Body Type', 'Price']).size().unstack(level=2, fill_value=0)

insight37 = (insight37.stack().reset_index(name='Total Count'))

insight37['Percentage'] = ((insight37['Total Count'] / 1099)*100).round(2)
insight37['Percentage'] = insight37['Percentage'].astype(str) + '%'
insight_37 = insight37.nlargest(10,'Total Count')

pd.crosstab([insight37['Model'], insight37['Total Count']],[insight37['Body Type']],margins=True)

	Body Type	Cab Chassis	Convertible	Fastback	Hardtop - Coupe	Hatchback	Limousine	Open Wheeler	Panel Van	Roadster	Sedan	Soft Top	Style Side	Targa	Tourer	Tray	Ute	Van	Wagon	nan	All
Model	Total Count
COUPE Master 85	0	0	0	0	367	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	367
	3	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1
X7	0	0	0	0	0	0	0	0	0	367	0	0	0	0	0	0	0	0	0	0	367
	2	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1
1 SERIES E82 LCI MY13	0	0	0	0	367	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	367
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Cooper BRM F2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	367	367
	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1
Jaguar E-Type 4.2 F H Coupe	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	367	367
	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1
All		2944	28336	5152	58144	8096	2208	1472	3680	10672	71392	1472	736	1472	1104	1104	23184	8464	26128	19504	275264

1462 rows × 20 columns

fig = px.bar(insight_37,x="Model", y="Total Count", color="Body Type",barmode='group',text="Percentage",
             title = "Top 10 Car Model according to Body Type")
fig.show()

pd.crosstab([insight37['Model'], insight37['Total Count']],[insight37['Price']],margins=True)

	Price	0	1950	2500	2800	3300	3990	4500	4950	5500	5990	...	41999000	46599000	47999000	49999000	54999000	69999000	99099000	219999000	2000020000	All
Model	Total Count
COUPE Master 85	0	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	367
	3	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
X7	0	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	367
	2	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
1 SERIES E82 LCI MY13	0	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	367
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Cooper BRM F2	0	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	367
	1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
Jaguar E-Type 4.2 F H Coupe	0	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	367
	1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
All		748	748	748	748	748	748	748	748	748	748	...	748	748	748	748	748	748	748	748	748	275264

1462 rows × 369 columns

fig = px.bar(insight_37,x="Model", y="Total Count", color="Price",barmode='group',text="Percentage",
             title = "Top 10 Car Model according to Price")
fig.show()

Insight 38: Top 10 Body Type according to Fuel Type and Price

insight38 = df.groupby(['Body Type', 'Fuel Type', 'Price']).size().unstack(level=2, fill_value=0)

insight38 = (insight38.stack().reset_index(name='Total Count'))

insight38['Percentage'] = ((insight38['Total Count'] / 1099)*100).round(2)
insight38['Body Type'] = insight38['Body Type'].replace(r'nan', "Not available", regex=True)
insight38['Fuel Type'] = insight38['Fuel Type'].replace(r'nan', "Not available", regex=True)
insight38['Percentage'] = insight38['Percentage'].astype(str) + '%'
insight_38 = insight38.nlargest(10,'Total Count')

pd.crosstab([insight38['Body Type'], insight38['Total Count']],[insight38['Fuel Type']],margins=True)

	Fuel Type	Diesel	Dual Fuel	Ethanol	Hybrid	LPG	Leaded Petrol	Not available	Premium Unleaded	Unleaded	All
Body Type	Total Count
Cab Chassis	0	363	0	0	0	0	0	367	367	367	1464
	1	5	0	0	0	0	0	1	1	1	8
Convertible	0	367	0	0	0	0	365	346	350	337	1765
	1	1	0	0	0	0	0	13	10	20	44
	2	0	0	0	0	0	3	7	7	10	27
...	...	...	...	...	...	...	...	...	...	...	...
Wagon	1	12	0	0	0	0	1	14	13	7	47
	2	11	0	0	0	0	0	5	4	9	29
	3	1	0	0	0	0	0	0	0	1	2
	4	1	0	0	0	0	0	0	0	0	1
All		2944	1472	736	368	1472	4048	5520	5520	6256	28336

77 rows × 10 columns

fig = px.bar(insight_38,x="Body Type", y="Total Count", color="Fuel Type",barmode='group',text="Percentage",
             title = "Top 10 Car Body Type according to Fuel Type")
fig.show()

pd.crosstab([insight38['Body Type'], insight38['Total Count']],[insight38['Price']],margins=True)

	Price	0	1950	2500	2800	3300	3990	4500	4950	5500	5990	...	41999000	46599000	47999000	49999000	54999000	69999000	99099000	219999000	2000020000	All
Body Type	Total Count
Cab Chassis	0	4	4	4	4	4	4	4	4	4	3	...	4	4	4	4	4	4	4	4	4	1464
	1	0	0	0	0	0	0	0	0	0	1	...	0	0	0	0	0	0	0	0	0	8
Convertible	0	5	5	5	5	5	5	5	5	5	5	...	5	5	5	5	5	5	5	5	5	1765
	1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	44
	2	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	27
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Wagon	1	0	1	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	47
	2	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	29
	3	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	2
	4	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
All		77	77	77	77	77	77	77	77	77	77	...	77	77	77	77	77	77	77	77	77	28336

77 rows × 369 columns

fig = px.bar(insight_38,x="Body Type", y="Total Count", color="Price",barmode='group',text="Percentage",
             title = "Top 10 Car Body Type according to Price")
fig.show()

Insight 39: Top 10 Car Model according to Fuel Type and Price

insight39 = df.groupby(['Model', 'Fuel Type', 'Price']).size().unstack(level=2, fill_value=0)

insight39 = (insight39.stack().reset_index(name='Total Count'))

insight39['Percentage'] = ((insight39['Total Count'] / 1099)*100).round(2)
insight39['Fuel Type'] = insight39['Fuel Type'].replace(r'nan', "Not available", regex=True)
insight39['Percentage'] = insight39['Percentage'].astype(str) + '%'
insight_39 = insight39.nlargest(10,'Total Count')

pd.crosstab([insight39['Model'], insight39['Total Count']],[insight39['Fuel Type']],margins=True)

	Fuel Type	Diesel	Dual Fuel	Ethanol	Hybrid	LPG	Leaded Petrol	Not available	Premium Unleaded	Unleaded	All
Model	Total Count
COUPE Master 85	0	0	0	0	0	0	0	0	0	367	367
	3	0	0	0	0	0	0	0	0	1	1
X7	0	0	0	0	0	0	0	0	367	0	367
	2	0	0	0	0	0	0	0	1	0	1
1 SERIES E82 LCI MY13	0	0	0	0	0	0	0	367	0	0	367
...	...	...	...	...	...	...	...	...	...	...	...
Cooper BRM F2	0	0	0	0	0	0	0	367	0	0	367
	1	0	0	0	0	0	0	1	0	0	1
Jaguar E-Type 4.2 F H Coupe	0	0	0	0	0	0	0	367	0	0	367
	1	0	0	0	0	0	0	1	0	0	1
All		24288	1840	736	368	1840	15824	108560	60720	63664	277840

1462 rows × 10 columns

fig = px.bar(insight_39,x="Model", y="Total Count", color="Fuel Type",barmode='group',text="Percentage",
             title = "Top 10 Car Model according to Fuel Type")
fig.show()

pd.crosstab([insight39['Model'], insight39['Total Count']],[insight39['Price']],margins=True)

	Price	0	1950	2500	2800	3300	3990	4500	4950	5500	5990	...	41999000	46599000	47999000	49999000	54999000	69999000	99099000	219999000	2000020000	All
Model	Total Count
COUPE Master 85	0	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	367
	3	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
X7	0	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	367
	2	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
1 SERIES E82 LCI MY13	0	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	367
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Cooper BRM F2	0	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	367
	1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
Jaguar E-Type 4.2 F H Coupe	0	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	367
	1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
All		755	755	755	755	755	755	755	755	755	755	...	755	755	755	755	755	755	755	755	755	277840

1462 rows × 369 columns

fig = px.bar(insight_39,x="Model", y="Total Count", color="Price",barmode='group',text="Percentage",
             title = "Top 10 Car Model according to Price")
fig.show()

Insight 40: Top 10 Models according to Listing Type and Price

insight40 = df.groupby(['Model', 'Listing Type', 'Price']).size().unstack(level=2, fill_value=0)

insight40 = (insight40.stack().reset_index(name='Total Count'))

insight40['Percentage'] = ((insight40['Total Count'] / 1099)*100).round(2)
insight40['Listing Type'] = insight40['Listing Type'].replace(r'nan', "Not available", regex=True)
insight40['Percentage'] = insight40['Percentage'].astype(str) + '%'
insight_40 = insight40.nlargest(10,'Total Count')

pd.crosstab([insight40['Model'], insight40['Total Count']],[insight40['Listing Type']],margins=True)

	Listing Type	New	Not available	Used	All
Model	Total Count
COUPE Master 85	0	0	0	367	367
	3	0	0	1	1
X7	0	0	0	367	367
	2	0	0	1	1
1 SERIES E82 LCI MY13	0	0	0	367	367
...	...	...	...	...	...
Cooper BRM F2	0	0	367	0	367
	1	0	1	0	1
Jaguar E-Type 4.2 F H Coupe	0	0	367	0	367
	1	0	1	0	1
All		1104	19504	241040	261648

1462 rows × 4 columns

fig = px.bar(insight_40,x="Model", y="Total Count", color="Listing Type",barmode='group',text="Percentage",
             title = "Top 10 Car Model according to Year")
fig.show()

pd.crosstab([insight40['Model'], insight40['Total Count']],[insight40['Price']],margins=True)

	Price	0	1950	2500	2800	3300	3990	4500	4950	5500	5990	...	41999000	46599000	47999000	49999000	54999000	69999000	99099000	219999000	2000020000	All
Model	Total Count
COUPE Master 85	0	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	367
	3	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
X7	0	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	367
	2	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
1 SERIES E82 LCI MY13	0	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	367
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Cooper BRM F2	0	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	367
	1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
Jaguar E-Type 4.2 F H Coupe	0	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	367
	1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
All		711	711	711	711	711	711	711	711	711	711	...	711	711	711	711	711	711	711	711	711	261648

1462 rows × 369 columns

fig = px.bar(insight_40,x="Model", y="Total Count", color="Price",barmode='group',text="Percentage",
             title = "Top 10 Car Model according to Price")
fig.show()